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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

2022 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2022 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and heavy-duty vehicle technologies; scenarios for conventional and alternative fuels. It details the assumptions used to calculate those costs, such as natural gas and electricity prices, discount rates, and vehicle miles traveled. The 2022 Transportation ATB vehicle data are specifically for cars powered by gasoline, diesel, natural gas, gasoline hybrid, plug-in hybrid, battery electric, and fuel-cell powertrains and for trucks powered by diesel, diesel hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Fuels and blendstocks include gasoline, ethanol, blendstock for oxygenate blending, diesel, diesel from biomass, natural gas, electricity, hydrogen, aviation fuel, and marine fuel. At this time, the ATB does not include other vehicles such as buses, 2- and 3-wheeled motorized vehicles, or non-road vehicles such as aircraft, vessels, locomotives, and those for industry and agriculture. See "ATB Transportation Website" resource below for more project information.

2022↗
DOE OSTI2024

2024 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2024 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and heavy-duty vehicle technologies; scenarios for conventional and alternative fuels. It details the assumptions used to calculate those costs, such as natural gas and electricity prices, discount rates, and vehicle miles traveled. The 2024 Transportation ATB vehicle data are specifically for cars powered by gasoline, diesel, natural gas, gasoline hybrid, plug-in hybrid, battery electric, and fuel-cell powertrains and for trucks powered by diesel, diesel hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Fuels and blendstocks include gasoline, ethanol, blendstock for oxygenate blending, diesel, diesel from biomass, natural gas, electricity, hydrogen, aviation fuel, and marine fuel. At this time, the ATB does not include other vehicles such as 2- and 3-wheeled motorized vehicles, or non-road vehicles such as aircraft, vessels, locomotives, and those for industry and agriculture. See "Transportation ATB Website" resource below for more project information.

2024↗
DOE OSTI2021

Performance and cost of fuel cells for urban air mobility

Several companies are developing enabling elements of urban air mobility (UAM) for air taxis, including prototypes of electric vertical take-off and landing (eVTOL) vehicles. These prototypes incorporate electric and hybrid powertrains for multi-rotor and tilt-rotor crafts. Many eVTOLS are using batteries for propulsion and charging them rapidly between the flights or swapping them for slow charging overnight. Rapid charging degrades the battery cycle life while swapping requires multiple batteries and charging stations. This study has conducted a technoeconomic evaluation of the eVTOL air taxis with alternate powertrains using hydrogen fuel cell systems being developed for light-duty and heavy-duty vehicles. We consider performance metrics such as fuel cell engine power, weight, and durability; hydrogen consumption and weight of storage system; and maximum take-off weight. The metrics for economic evaluation are capital cost, operating and maintenance cost, fuel cost, and the total cost of ownership (TCO). In this work, we compare the performance and TCO of battery, fuel cell and fuel cell – battery hybrid powertrains for multi-rotor and tilt-rotor crafts. We show that fuel cells are the only viable concept for powering multi-rotor eVTOLs on an urban scenario that requires 60-mile range, and hybrid fuel cells are superior to batteries as powertrains for tiltrotor eVTOLs

08 HYDROGEN↗
DOE OSTI2020

Solving Eco-Driving Problems Using Indirect Collocation Method and Smooth Representation

Here, we discuss the eco-driving problem, considering both electric and conventional powertrains, and presents a pathway to solving it numerically using an indirect collocation method. Despite the low-order system dynamics, the piecewise fuel/efficiency map, gear shifting, and real-world traffic/road situations bring system discontinuities/switchings and pure state constraints into the problem formulation, which make the problem highly nonlinear and nontrivial to solve. This letter introduces smooth approximations to convert the original problem to an unconstrained (and penalized) smooth boundary-value problem. This approach eliminates the discussion of the switching structure and leads to a lightweight Newton-method-based solution procedure.

42 ENGINEERING↗
DOE OSTI2021

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗
DOE OSTI2023

Integrated Optimization of Powertrain Energy Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs) and autonomous vehicles have been widely studied recently for on-road transportation. In the study of autonomous HEVs, the control of the vehicle's external dynamics and powertrain dynamics are often treated separately. Optimizing these two problems together can significantly improve fuel economy. In this paper, an autonomous HEV following a leader is considered. First, the augmented model to integrate the abovementioned dynamics is presented. Second, the optimization problem is defined to find the optimum fuel consumption of the follower in pursuit of a leader in a drive cycle. A customized control strategy based on Approximate Dynamic Programming (ADP) is then explored in which the optimal cost-to-go at each time step is approximated using neural networks. Also, the accuracy of the optimization solution is enhanced by applying the concept of the reachable sets. At last, three case studies show that the examined integrated control strategy outperforms the one with the separated optimization method by an additional 7.4%, 4.6%, and 11.8% improvement in fuel consumption, respectively.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Energy Consumption, Performance, and Cost Estimates for Medium and Heavy-Duty Vehicles Based on 2022 Assumptions

Assumptions for this work was collected and the analysis was completed in FY22. This contains information for more than 20 types of medium and heavy duty vehicles. Vehicles with various levels of hybridization, electric and fuel cell powertrains are considered in this work. More details are available in the report published by Argonne accessible from https://vms.taps.anl.gov/research-highlights/u-s-doe-vto-hfto-r-d-benefits/. TechScape, a convenient data visualization tool is also provided by Argonne for this data, accessible from [TechScape Web](https://vms.taps.anl.gov/data/techscape-web-2023/).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Energy Optimization of Light and Heavy-Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data Sharing (Final Scientific and Technical Report)

Vehicle connectivity and automated driving technologies individually have the potential to decrease energy consumption and/or increase safety on light, medium or heavy duty vehicles to varying degrees depending on the traffic infrastructure and specific driving scenarios. Due to advances in sensing, perception and computing power, research and development emphasis in the mobility sector has shifted away from connectivity. Prior research has shown that driving automation with the absence of connectivity can in certain circumstances increase energy consumption. The effectiveness of synergizing connectivity and driving automation technologies is the focus of this work, specifically applied to vehicle cohorts of mixed composition, light and heavy duty, and powertrains ranging from all electric to conventional internal combustion engine. The project team is led by Michigan Technological University (MTU) and partnered with AVL Mobility Technologies Inc. (AVL), Borg Warner (BW), Traffic Technology Services (TTS), American Center for Mobility (ACM) and Navistar (NAV). The main thrusts for the team are to develop a micro-traffic simulation environment with specific VD&PT system attributes and CAV capabilities, 2) field a vehicle test fleet of mixed classification, propulsion and CAV capacity, 3) develop artificial intelligence (AI) and machine learning (ML) based multi-agent optimization methods for various traffic infrastructures, 4) integrate the virtual environment and the optimization methods then deploy the system as a CAV hardware in the loop (HiL) for the vehicle test fleet and 5) conduct closed track and public road testing to validate simulation and demonstrated energy and mobility improvements at multiple scales. For a cohort of mixed vehicles, the team will demonstrate a reduction of energy consumption of 10-50% at intersection, arterial roadway and limited access highway scenarios through connectivity and automation in simulation and at a closed test track. The energy reduction objectives of the project are summarized in Table 1, indicating the infrastructure and over what distances are relevant considered. Single scenario energy reductions are not relevant and thus, the research team took the approach to vary parameters associated with the infrastructure, vehicle cohort composition and dynamic behavior to generate energy consumption distributions for both unconnected and connected scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Power Split Supercharging: A Mild Hybrid Approach to Boost Fuel Economy

This work investigates an innovative low-voltage (<60 V) hybrid device that enables engine boosting and downsizing in addition to mild hybrid functionalities such as regenerative braking, start-stop, and torque assist. A planetary gear set and a brake permit the power split supercharger (PSS) to share a 9 kW motor between supercharging the engine and direct torque supply to the crankshaft. In contrast, most e-boosting schemes use two separate motors for these two functionalities. This single motor structure restricts the PSS operation to only one of the supercharging or parallel hybrid modes; therefore, an optimized decision making strategy is necessary to select both the device mode and its power split ratio. An adaptive equivalent consumption minimization strategy (A-ECMS), which uses the battery state of charge (SoC) history to adjust the equivalence factor, is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces vehicle fuel consumption by 18.4% over standard FTP75 cycle, compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to the baseline.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Future Cost Benefits Analysis for Electrified Vehicles from Advances Due to U.S. Department of Energy Targets

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Characterizing the Payback and Profitability for Automated Heavy Duty Vehicle Platooning

Heavy duty vehicle platooning under highway operating conditions has been projected to provide significant fuel economy gains based on aerodynamic drag improvements of the platooning vehicles. Realizing these benefits and the economic viability under real-world operating conditions presents several challenges. The objective of this paper (the third as part of a series) is to analytically quantify the payback and profitability of heavy-duty vehicles platooning across the U.S. Interstate highway system. In this paper, a rigorous assessment of several factors that influence the platooning system payback for an end-user as well as the revenue potential for suppliers who may be utilizing an equipment lease model dependent on end-user savings, is presented. In this assessment key interactions explored include market adoption rates, platooning velocities, platoon-able daily mileage, platooning likelihood, variations in baseline powertrain fuel economy (diesel or electric), price of fuel (diesel or electricity), platooning fuel economy benefits, price of the added technology, and the impact of natural platooning due to traffic interactions. Further, the paper explores the economic impact of higher levels of vehicle automation for the trailing vehicles in the platoon, where extending the driver Hours of Service (HoS) may provide additional financial benefits. While the approach makes use of a limited fidelity vehicle analytical model for longitudinal dynamics and operations economics, the narrative provides application decision personnel with a mechanism and well-defined set of impact factors to consider as part of their architectural selection process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Modeling Freight Traffic Demand and Highway Networks for Hydrogen Fueling Station Planning: A Case Study of U.S. Interstate 75 Corridor

The use of hydrogen as an alternative transportation fuel has gained much interest in recent years. Hydrogen can be utilized in electric vehicles equipped with hydrogen powertrains (including hydrogen internal combustion engines or fuel cells). Given that most of the freight in the U.S. is transported via diesel trucks, transition to hydrogen fuel would help achieve significant environmental benefits as well as accelerate the decarbonization of the freight transportation sector. This paper presents the methodology and results of a case study on modeling freight traffic demand and highway networks based on publicly available data for the Interstate 75 freight corridor. The purpose of this study is to prepare input traffic and network data that can support the planning of a hydrogen fueling station infrastructure. In particular, the data can be used for siting and characterizing an optimized framework of hydrogen fueling stations from candidate diesel stations along the Interstate 75 corridor. The methodologies developed and presented in this paper may be readily expanded and applied to any transport corridor given the data availability. This paper is the first in a series that will build out a comprehensive model to optimize a consolidated national hydrogen refueling infrastructure eco-system targeted at commercial vehicles.

Uddin, Majbah↗
DOE OSTI2026

Substitution or Shared Utilization? Intrahousehold Vehicle Use in Mixed-Powertrain Households

While previous research has focused heavily on understanding the factors deriving alternative fuel vehicle adoption rates, there remains a significant gap in understanding how households distribute mileage across different powertrains. This study utilizes data from the 2022 Next Generation National Household Travel Survey to investigate vehicle miles traveled within a sample of 150 plug-in electric vehicle (PEV)-owning households (in which at least one battery electric vehicle is present), characterizing how different powertrains are integrated into daily mobility. Leveraging a Seemingly Unrelated Regression (SUR) framework the study jointly models the utilization of PEVs, hybrid electric vehicles (HEV), and internal combustion engine vehicles (ICEVs) while accounting for household-level substitution effects. The results provide evidence of an asymmetric substitution effect. In households with mixed-powertrain configurations, the ICEV captures a substantially higher share of household miles (compared with the PEV), acting as a utility sponge. Conversely, the model identifies specific socioeconomic and geographic cohorts that prioritize PEV as the primary household workhorse, indicating a systematic sorting effect. Although the sample size limits broader generalizability, these findings suggest that PEVs are used for frequent, specific routine-intensive roles, whereas the ICEV remains a specialized utility vehicle. These insights highlight distinct intrahousehold vehicle use behaviors that are often obscured by aggregate fleetwide statistics.

25 ENERGY STORAGE↗
DOE OSTI2023

Demonstration of Ego Vehicle and System Level Benefits of Eco-Driving on Chassis Dynamometer

Eco-Driving with connected and automated vehicles has shown potential to reduce energy consumption of an individual (i.e., ego) vehicle by up to 15%. In a project funded by ARPA-E, a team led by Southwest Research Institute demonstrated an 8-12% reduction in energy consumption on a 2017 Prius Prime. This was demonstrated in simulation as well as chassis dynamometer testing. The authors presented a simulation study that demonstrated corridor-level energy consumption improvements by about 15%. This study was performed by modeling a six-kilometer-long urban corridor in Columbus, Ohio for traffic simulations. Five powertrain models consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles were developed. The design of experiment consisted of sweeps for various levels of traffic, penetration of smart vehicles, penetration of technology, and powertrain electrification. The large-scale simulation study consisted of doing approximately 96,000 powertrain simulations. A sophisticated clustering scheme was built and utilized to down select representative traces for each scenario from the simulation study for vehicle testing on a chassis dynamometer. Furthermore, this paper provides a summary of individual ego vehicle testing as well as a comprehensive overview of the method utilized for down selecting representative traces from large scale simulation studies that can be used to quantify corridor level benefits. Vehicle test results along with corresponding analyses are presented.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A Comprehensive Simulation Study to Evaluate Future Vehicle Energy and Cost Reduction Potential

Under the umbrella of EERE’s Office of Sustainable Transportation, the U.S. Department of Energy’s (DOE) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) seek to develop sustainable, affordable, and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate the potential for vehicle-level fuel savings is critical to understanding DOE’s impact and success in this mission For this study, Argonne National Laboratory (Argonne) simulated technologies funded by VTO and HFTO for light-duty vehicles across the following: Powertrain configurations (conventional, power-split hybrid electric vehicle, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles); Vehicle classes (compact car, mid-size car, small sport utility vehicle [SUV], mid-size SUV, and pickup truck); Fuels (gasoline, diesel, natural gas, hydrogen, and battery electricity). We assessed each technology for five different timeframes: laboratory years 2015 (reference), 2020, 2025, 2030, and 2045. We assumed a delay of 5 years between laboratory year and model year (i.e., the year the technology is introduced into production). Finally, we included uncertainties for both technology performance and cost by considering two cases (note that these cases are not intended as predictions of future performance): Low case , aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on business as usual regulatory and market environments; High case , aligned with aggressive technology advancements based on research and development (R&D) targets developed through support by VTO and HFTO. We estimated the energy and cost impact of different technologies using Autonomie (Argonne undated), a state-of-the-art vehicle system simulation tool developed by Argonne and used to assess the energy consumption, performance, and cost of multiple advanced vehicle technologies. The tool comprises a complete set of vehicle models to assess impacts across a wide range of classes (from light- to heavy-duty), powertrain configurations (from conventional to hybrid electric vehicles [HEVs], fuel cell electric vehicles [FCEVs], plug-in hybrid electric vehicles [PHEVs], and battery electric vehicles [BEVs]), components, and control strategies, including vehicle-level and component-level controls developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies: analyzing various component technologies, sizing powertrain components to meet different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route-based vehicle energy control, and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions made and the vehicle-level energy consumption benefits and associated technology costs estimated for various types of light-duty vehicles. Details regarding vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

08 HYDROGEN↗